saturation-analysis

name	saturation-analysis
description	Track score trajectories, detect saturation/failure points — 15 benchmarks, 50 papers, 60 web searches
used-by	benchmark-archaeology

Saturation Analysis Strategy

Track benchmark score trajectories over time to detect saturation signals, ceiling effects, and inflection points that indicate a benchmark has lost discriminative power.

Purpose

Determine which benchmarks are approaching or have reached saturation, quantify remaining headroom, estimate time-to-ceiling, and identify the specific failure modes that remain unsolved even at high aggregate scores.

Budget

Resource	Floor	Target
Benchmarks analyzed	10	15
Papers read	35	50
Web searches	40	60

State Ledger

<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Benchmarks analyzed | 0 | 15 | PENDING |
| Score trajectories built | 0 | 15 | PENDING |
| Papers fetched | 0 | 50 | PENDING |
| Papers read | 0 | 35 | PENDING |
| Web searches | 0 | 60 | PENDING |
| Saturation detections run | 0 | 15 | PENDING |
| Leaderboard analyses done | 0 | 10 | PENDING |
| Failure mode catalogs built | 0 | 5 | PENDING |
</HARD-GATE>

Cannot exit until 80% of all targets met.

Available Tactics

• score-trajectory-analysis — Collect historical scores, fit saturation curves, detect inflection points

Available SOPs

• benchmark-inventory — Identify target benchmarks in domain
• metric-decomposition — Analyze metric properties (ceiling effects, granularity)
• leaderboard-dynamics-analysis — Analyze score distributions and compression
• saturation-detection (shared from literature-survey) — Detect saturation signals
• benchmark-synthesis — Produce cross-benchmark saturation report

Execution Guidance

1. Inventory Phase: Use benchmark-inventory to identify 15 benchmarks spanning different maturity levels
2. Data Collection (per benchmark): a. Search leaderboards (Papers With Code, official sites) for historical scores b. Collect papers reporting SOTA results chronologically c. Note human baselines, random baselines, and theoretical ceilings
3. Trajectory Analysis (per benchmark): a. Run score-trajectory-analysis tactic to build time-series and fit curves b. Run saturation-detection to classify saturation status c. Run leaderboard-dynamics-analysis for score compression analysis
4. Failure Mode Mining: For saturated benchmarks, identify remaining hard subsets
5. Synthesis: Cross-benchmark comparison of saturation timelines and patterns

Output Format

saturation_report:
  benchmark_name: string
  saturation_status: pre-saturation|approaching|saturated|supersaturated
  current_sota: float
  human_baseline: float
  theoretical_ceiling: float
  headroom_remaining: float
  estimated_time_to_ceiling: string  # e.g., "6-12 months"
  inflection_points: list[{date, score, cause}]
  score_compression: float  # top-10 score range
  remaining_hard_subsets: list[string]
  successor_benchmarks: list[string]